Abstract: A non-transitory computer-readable recording medium stores therein a training program that causes a computer to execute a process including first inputting a first image capturing a target compound to an encoder of an auto-encoder including a latent space that is isometric with respect to an input space, second inputting a latent variable output by the encoder and a typical compound model corresponding to a typical case of a three-dimensional structure of the target compound to a decoder of the auto-encoder, and updating parameters of the encoder and the decoder, based on a reconfiguration error between a second image reconfigured based on an output of the encoder and the first image.

Abstract: A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process including acquiring a distribution of latent variables output from an encoder in a process of training an autoencoder, generating a path related to deformation based on the distribution of the latent variables, selecting, from a plurality of latent variables generated by inputting a plurality of particle images to the trained encoder, a plurality of neighboring latent variables of which a distance to the path is less than a threshold, selecting a plurality of neighboring particle images corresponding to the plurality of neighboring latent variables among the plurality of particle images, and estimating a plurality of three-dimensional atom models based on the plurality of neighboring particle images.

Abstract: A computer-readable recording medium stores therein a control program that causes a computer to execute a process, the process including: generating an intermediate representation corresponding to input data, the intermediate representation being generated using a trained model; training a distribution of latent representations corresponding to the generated intermediate representation, using a predetermined encoder generating the latent representation from the intermediate representation and a predetermined decoder corresponding to the predetermined encoder and generating an intermediate representation different from the intermediate representation; selecting, from the trained distribution, a sample of the latent representation based on a probability distribution; generating, using the predetermined decoder, a new intermediate representation corresponding to the selected sample; and generating, using the trained model, output data corresponding to the generated new intermediate representation.

Abstract: An information processing apparatus inputs first data to an encoder to generate second data. The information processing apparatus adds noise whose magnitude is equal to or smaller than a threshold to the second data to generate third data. The information processing apparatus inputs the third data to a decoder to generate fourth data. The information processing apparatus performs training of the encoder and the decoder based on a loss function including an error term indicative of an error between the first data and the fourth data and a correction term indicative of a probability calculated from the second data by using a plurality of probability distributions each having a variance according to the threshold.

Abstract: A computer generates graph data including a plurality of nodes representing different features and a plurality of edges connecting the plurality of nodes in a latent space to which features each corresponding to shape data representing a shape of a molecule and having a smaller number of dimensions than the shape data belong. The computer calculates a weight for each of the plurality of edges, using the distance in the latent space between two nodes connected by the edge and a probability distribution of the features in the latent space. The computer predicts a deformation process between a first shape corresponding to a first node among the plurality of nodes and a second shape corresponding to a second node among the plurality of nodes by searching for a path in the latent space between the first node and the second node using the weights.

Abstract: A non-transitory computer-readable recording medium stores therein a machine learning program that causes a computer to execute a process including acquiring a second frequency image by inputting output of an encoder that has input a first frequency image to a decoder, and training the encoder and the decoder based on a loss function in which a weight related to a first frequency is smaller than a weight related to a second frequency higher than the first frequency, the first frequency image, and the second frequency image.

Abstract: A non-transitory computer-readable recording medium storing a control program for causing a computer to execute a process includes, when an intermediate representation of input data input to a learned model is changed, evaluating validity of current output data generated from the intermediate representation by the learned model based on a first indicator correlated with an existence probability of output data in a data distribution of learning data used for learning of the learned model, and changing the intermediate representation based on an evaluation result such that validity of output data generated by the learned model increases.

Abstract: A non-transitory computer-readable recording medium stores therein a path selecting program that causes a computer to execute a process including extracting a plurality of representative points from a presence probability distribution of states of a target, identifying a first plurality of state transition paths between the plurality of representative points, and selecting a second plurality of state transition paths from the first plurality of state transition paths, based on a probability density of each path included in the first plurality of state transition paths.

Abstract: A non-transitory computer-readable recording medium stores therein an information output program that causes a computer to execute a process including acquiring a first point and a second point in a presence probability distribution of a state of an object, specifying a plurality of points serving as candidates for a transition destination from the first point, selecting a third point from the points based on a distance from the first point to each of the points and probability during transition, and outputting a transition path from the first point to the second point including the third point as state transition information on the object.

Abstract: A new compound inhibiting phosphorylation of Ser727 of STAT3, a phosphorylation inhibitor containing the new compound, an insulin resistance improving agent and a preventive or therapeutic agent for diabetes; and a screening method for at least one of the insulin resistance improving agent and the preventive or therapeutic agent for diabetes.

Abstract: A new compound inhibiting phosphorylation of Ser727 of STAT3, a phosphorylation inhibitor containing the new compound, an insulin resistance improving agent and a preventive or therapeutic agent for diabetes; and a screening method for at least one of the insulin resistance improving agent and the preventive or therapeutic agent for diabetes.

Abstract: A new compound inhibiting phosphorylation of Ser727 of STAT3, a phosphorylation inhibitor containing the new compound, an insulin resistance improving agent and a preventive or therapeutic agent for diabetes; and a screening method for at least one of the insulin resistance improving agent and the preventive or therapeutic agent for diabetes.